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SkillsMP has collected 213 skills from Pavel-Kravchenko/Bioinformatics. Open a skill to review its source and details.
Pavel-Kravchenko/BioinformaticsShowing 40 of 213 collected skills.
Bottom-up DP (tabulation) in Python: edit distance/Levenshtein, LCS, and LIS with rolling-array space optimization. Use when comparing DNA/protein sequences, scoring similarity, or filling a DP table without recursion.
Order vertices of a directed acyclic graph (DAG) with DFS-based or Kahn's BFS-based topological sort, detect cycles, and compute critical-path/makespan for weighted task DAGs. Use when scheduling a gene regulatory cascade, metabolic pathway, or bioinformatics…
Implement a trie (prefix tree) in Python for O(m) word insert/search, O(p) prefix checks, and O(p+k) prefix enumeration; build autocomplete, spell-checkers, and k-mer/gene-name lookup over DNA or dictionary strings. Use when asked for prefix tree, trie data…
Predict protein 3D structure with AlphaFold2/ColabFold/ESMFold, fetch precomputed models from the AlphaFold DB, and interpret pLDDT/PAE confidence metrics and Cα RMSD. Use when predicting a structure from sequence, asking "how confident is this AlphaFold…
Analyze ATAC-seq BAM/BED data with pysam and pybedtools — fragment-size QC, NFR fraction, Tn5 +4/-5 offset correction, and TF footprint scoring around motif sites. Use when doing ATAC-seq QC, computing nucleosome-free-region fraction, correcting Tn5 insertion…
Assemble genomes de novo: greedy OLC, de Bruijn graph/Eulerian path, N50/L50/NG50 stats, SPAdes/Flye/hifiasm CLI usage. Use when choosing k-mer size, picking an assembler for Illumina/ONT/HiFi reads, or scoring contiguity.
Assemble shotgun metagenomic reads with MEGAHIT, bin contigs with MetaBAT2/CONCOCT/MaxBin2+DAS_Tool, grade MAGs with CheckM/MIMAG tiers. Use for metagenome assembly, contig binning, or MAG recovery.
Assemble ONT/HiFi reads with Flye/Hifiasm, polish with Medaka, QC with QUAST/BUSCO, call SVs (DEL/INS/INV/DUP/BND) with Sniffles2. Use for long-read assembly, N50/BUSCO QC, or nanopore/HiFi SV calling to VCF.
Fit Bayesian models with PyMC/Bambi/ArviZ: NUTS sampling, prior/posterior checks, HDI intervals, hierarchical GLMMs, LOO/WAIC comparison. Use when doing Bayesian inference, hierarchical modeling, or MCMC diagnostics.
Parse/write FASTA, FASTQ, SAM/BAM, VCF, BED, GFF/GTF with pysam and pure Python; decode SAM FLAG/CIGAR; reconcile 0-based vs 1-based coordinates. Use for custom format parsers or off-by-one coordinate bugs.
Fit Michaelis-Menten Vmax/Km with scipy curve_fit, convert absorbance to concentration via Beer-Lambert, and model enzyme inhibition. Use when analyzing enzyme assays or estimating Km, Vmax, kcat, or Ki.
Classify tumor RNA-seq into subtypes (melanoma Tirosh/Harbst on TCGA-SKCM): log1p/z-score, PCA/t-SNE, hierarchical clustering, random forest, Kaplan-Meier survival. Use when subtyping cBioPortal expression data.
BLAST-identify unknown DNA/CDS with Biopython, QC/translate sequences, build NJ/UPGMA trees, and scan protein motifs. Use for sequence-to-discovery capstones, unknown-sequence ID, or FASTA-BLAST-tree pipelines.
Annotate scRNA-seq Leiden/Louvain clusters into cell types via canonical marker scoring, SingleR reference correlation, and CellTypist logistic-regression classification on an AnnData/SingleCellExperiment object. Use when doing cell type annotation, labeling…
FASTQ-to-peaks ChIP-seq pipeline: Bowtie2 align, Picard dedup, MACS2/MACS3 narrow/broad peak calling, FRiP/IDR QC, deepTools bamCoverage/heatmaps. Use for ChIP-seq/CUT&RUN peak calling or FRiP/NRF/IDR QC.
Normalize CITE-seq ADT counts (CLR/DSB) and build WNN graphs joining RNA+protein or RNA+ATAC with muon/Seurat FindMultiModalNeighbors. Use for CITE-seq, 10x Multiome, or paired RNA+ADT/ATAC single-cell integration.
Classify germline variant pathogenicity with ACMG/AMP 5-tier criteria (PVS1/PS1-4/PM1-6/PP1-5/BA1/BS1-4/BP1-7), query ClinVar via NCBI E-utilities, and filter by gnomAD population frequency to draft a clinical variant report. Use when doing ACMG…
Call CNVs from binned read-depth: GC-bias normalization, circular binary segmentation (CBS), log2-ratio-to-CN-state calling, tumor-purity correction. Use for CNV/copy-number segmentation, log2 ratio analysis, or gain/deletion/amplification calls.
Generate normalized bigWig coverage tracks from BAM with deepTools bamCoverage/bamCompare (RPKM/CPM/RPGC), summarize with multiBamSummary, and plot TSS/region signal with computeMatrix + plotHeatmap/plotProfile; pyBigWig for programmatic access. Use when…
Harmonize multi-omics data (RNA-seq, proteomics, methylation, metabolomics) before integration — per-layer normalization, KNN/half-minimum missing-value imputation, PCA/PVCA batch-effect detection, and ComBat correction with pandas/scikit-learn. Use when…
Train PyTorch CNN/LSTM/Transformer/VAE on DNA/protein sequences: one-hot encoding, motif filters, saliency. Use when classifying sequences, predicting TF binding sites, denoising scRNA-seq, or choosing DL vs ML.
Find ChIP-seq/ATAC-seq peaks that gain or lose signal between conditions using DiffBind (dba.count/dba.normalize/dba.analyze with DESeq2 or edgeR) on a consensus peak set, then annotate hits to genes/promoters with ChIPseeker annotatePeak. Use when comparing…
Compute PCA/UMAP embeddings and Leiden clusters for scRNA-seq with scanpy/Seurat; tune n_pcs/n_neighbors/resolution, find markers via rank_genes_groups. Use for UMAP plots, clustering single-cell data, or picking PCs/resolution.
Call DMRs from WGBS/RRBS beta values via BSmooth smoothing/t-stats or DSS/methylKit (R); annotate to promoters/CpG islands, correlate with RNA-seq log2FC. Use for DMR calling, DSS callDMR, or methylation-expression integration.
Dock ligands into a receptor with AutoDock Vina: build PDBQT files (Open Babel/RDKit), set the grid box, run vina, parse/rank poses by affinity and RMSD. Use for molecular docking, virtual screening, redocking a co-crystal ligand, or PDB/SMILES to PDBQT…
Fit Michaelis-Menten/Hill kinetics with scipy curve_fit; get Vmax/Km/kcat with bootstrap CIs, classify enzyme inhibition type. Use for enzyme assay data, saturation curves, Ki estimation, kcat/Km efficiency.
Compute DNA methylation age (Horvath/Hannum/GrimAge/PhenoAge elastic-net clocks) from 450K/EPIC beta values and epigenetic age acceleration (EAA). Use for DNAm clock scoring or EAA vs smoking/BMI/disease/mortality tests.
Read FCS 2.0/3.0/3.1 files with FlowKit/flowio, apply spillover compensation, logicle/arcsinh transforms, build gating hierarchies, and compute population statistics. Use when analyzing flow cytometry data, .fcs files, panels, compensation matrices, or gating…
Profile MetaCyc pathways/genes from metagenomes with HUMAnN3, test differential abundance via MaAsLin2, detect AMR genes with AMRFinderPlus. Use for metagenomic functional annotation, pathway abundance, or AMR profiling.
Infer TF-target regulatory networks via correlation, ARACNE mutual information, and GENIE3 random-forest importance; find feed-forward loops; validate against TRRUST. Use when building a GRN or asked about GENIE3, ARACNE, or regulons.
Simulate restriction digests, overhang compatibility, and primer Tm (Wallace/SantaLucia NN) in Python; plot agarose gel bands. Use when planning cloning, enzyme compatibility, or PCR primer design for a target Tm.
Implement OLC and de Bruijn assembly algorithms, compute N50/L50/NG50 stats, and run SPAdes/Flye/hifiasm on Illumina/HiFi/ONT reads. Use for k-mer graphs, comparing assemblers, or a FASTQ-to-contigs pipeline.
Run GWAS: SNP QC (MAF/HWE), PCA ancestry covariates, per-SNP logistic/linear regression, Manhattan/QQ plots, genomic inflation, LD clumping. Use when doing GWAS, SNP-array association, or stratification correction.
Type HLA-A/B/C/DRB1 with OptiType/arcasHLA and predict peptide-MHC binding (NetMHCpan %Rank_EL/IC50) to rank neoantigens. Use for HLA typing, MHC binding, pVACseq, HLA LOH, or HLA-B*57:01 screening.
Analyze TCR/BCR repertoires with scirpy: import MiXCR/10x/AIRR clonotypes, define clonotypes, compute clonal expansion/diversity/VDJ usage. Use when analyzing scTCR-seq/scBCR-seq, clonotype tables, or CDR3 spectratypes.
Align ONT/PacBio long reads with Minimap2 splice, call isoforms with bambu (NDR), test differential isoform usage with DRIMSeq. Use for long-read transcriptomics, novel isoform calling, or DTU/isoform-switch analysis.
Preprocess raw LC-MS mzML with XCMS centWave peak picking, obiwarp RT alignment, gap filling, PQN/QC normalization, adduct grouping. Use when building an XCMS pipeline or preprocessing untargeted metabolomics runs.
Classify StringTie/gffcompare transcripts into lncRNA subtypes by class code/length/TPM, score coding potential with CPC2/CPAT, detect circRNAs via CIRI2 BSJ reads. Use for lncRNA annotation or circRNA calls.
Engineer k-mer/GC/CpG DNA features, train scikit-learn classifiers (LogisticRegression, RandomForest, SVC), evaluate with CV/ROC-AUC. Use for promoter/variant classifiers or model comparison on omics features.
Run MAGeCK count/test on pooled CRISPR sgRNA screens, scoring gene essentiality via RRA, FDR, and log2 fold-change. Use when analyzing CRISPR screen FASTQ/count data, calling essential or drug-resistance genes, or benchmarking vs DepMap.